The Empirical Study of SMEs Innovation and Performance Factors in Sichuan
Bibliographic record
Abstract
This article aims to assess the factors affecting the relationship between innovation and performance of SMEs, which has been subject to by SMEs Innovation Fund in 1999 and successfully passed the acceptance of funds to support SMEs innovation in Sichuan Province; Data from 174 SMEs in Sichuan Province and investigation of different properties ,factors influencing innovation and innovation performance; different ways of improving the performance of SMEs, all help the government to develop more targeted support of business development innovation policy. According to the research, SMEs innovation activities are mainly limited to family scales, focusing on improving quality, increasing output and technical standards. The study found that technology-based SMEs in Sichuan Province mainly purchase techniques from external sources. Their own innovation is not good. Innovation strategies are adapted to competition have better performance in business, as they have the capability to independently research and adapt to the new marketing economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".